Locomotive signal system based on wireless sensor network and data source

Abstract

A network database contains data items distributed within spatially adjacent trains, and these spatially adjacent trains are interconnected via communication links. A network database system is provided, containing data items distributed within spatially adjacent trains, and these spatially adjacent trains are interconnected via communication links. A method for constructing a network database is provided, comprising: pre-storing data items within a train; changing the train's geographical location by moving it to a spatially adjacent geographical location, and the trains being interconnected via communication links. A method for constructing a locomotive signaling system is provided, comprising: pre-storing data items within a train; changing the train's geographical location by moving it to a spatially adjacent geographical location, and the trains being interconnected via communication links. Using this invention, a train can reliably obtain the position and speed information of all spatially adjacent trains, enabling coordinated action of a distributed autonomous signaling group to achieve autonomous control and operational safety.

Description

[0001] This application is a divisional application of the invention entitled "Locomotive Signaling System Based on Wireless Sensor Network and Data Source", filed on May 26, 2020, with application number 2020104583052. Technical Field

[0002] This invention belongs to the field of railway transportation applications and involves geographic information systems, computer science and technology, physics and railway science and technology. Specifically, it relates to a locomotive signaling system based on wireless sensor networks and data sources. Background Technology

[0003] Locomotive signals are signals displayed in the driver's cab that indicate the operating conditions ahead of the train. With ground signals as the primary signaling system, locomotive signals serve as auxiliary signals. They automatically reflect the display status and operating conditions of the ground signals ahead of the train, instructing the train to proceed and, in conjunction with the automatic train stopping system, ensuring safe train operation. Currently, signaling is not yet the primary system, making it difficult to guarantee the safety of locomotive-controlled trains. The effectiveness of locomotive control depends on reliable locomotive signals and necessary overspeed protection data, such as turnout type and route length. Due to various interferences to track circuits and defects in coded signals, locomotive signals often experience problems such as flashing white lights and no signals in siding turnout areas. Therefore, until locomotive signals become the primary system, locomotive control cannot be considered safe.

[0004] Passenger stations on dedicated passenger lines are the nodes and hubs of the lines. Their throughput capacity determines the density of train services on the dedicated passenger lines. A small-group, high-density train operation mode is a powerful guarantee for realizing the public transportation of dedicated passenger lines and highlighting their competitive advantage in the passenger transport market. Improving the throughput capacity of dedicated passenger stations under certain equipment conditions is an urgent problem that the new generation of train operation control systems needs to solve.

[0005] In transportation organization under the condition of large-scale network operation, the impact of emergencies will spread along two dimensions: time and space and network space. With the spread of the impact of emergencies and the loss of network capacity, the formulation of high-speed railway operation rules becomes more complex. Solving the problem of the spread of the impact of emergencies and the loss of network capacity is an urgent problem that the new generation of train operation control system needs to solve. Summary of the Invention

[0006] To address, or at least partially address, the above problems, this invention provides a locomotive signaling system based on a wireless sensor network and data source. This system improves work efficiency and reduces the impact of local faults on the overall system by coordinating the actions and communication between a distributed autonomous signaling group and processing multiple signals in parallel. The system uses signals that collect spatiotemporal data from ground-based data sources and simultaneously act as sensor nodes and management objects in the wireless sensor network. These signals cooperate with networked signals within the network coverage area to monitor the signals and generate locomotive signals. The ground-based data source monitors the time of events when a train arrives at the geographical location of the data source and generates and outputs spatiotemporal data, which is then sent to the train's onboard signals.

[0007] On one hand, the present invention provides a network database including a train interval time data item, used to describe the time interval between events of a train tracking the nearest train ahead on the same track and the trains arriving at the same geographical location one after the other.

[0008] Preferably, the network database uses one of the following data elements: a data element composed of time-stamped data items representing the train interval time, geographic location element identifier code, and time of spatiotemporal data collection event, used to describe the attributes of the event where the train arrives at the geographic location of the data source; a data element composed of time-stamped data items representing the latitude and longitude of the data collector's geographic location at the time of the train interval time and the time of the spatiotemporal data collection event, used to describe the attributes of the event where the train arrives at the geographic location of the data source; a data element composed of time-stamped data items representing the train interval time, geographic location element identifier code, the time interval between the arrival of the train's head and tail at the same geographic location, and the time of the spatiotemporal data collection event, used to describe the attributes of the event where the train arrives at the geographic location of the data source; a data element composed of time-stamped data items representing the train interval time, the time interval between the arrival of the train's head and tail at the same geographic location, the latitude and longitude of the data collector's geographic location, and the time of the spatiotemporal data collection event. This data element describes the attributes of an event where a train arrives at the geographical location of a data source. It consists of several data items: a tracking train interval time, a geographical location feature identifier, and an identifier for the data collector; a tracking train interval time, the latitude and longitude of the data collector's location at the time of the data collection event, and the data collector's identifier; a tracking train interval time, the geographical location feature identifier, the time interval between the arrival of the front and rear of a train at the same geographical location, and the data collector's identifier; and a tracking train interval time, the time interval between the arrival of the front and rear of a train at the same geographical location, the latitude and longitude of the data collector's location at the time of the data collection event, and the data collector's identifier.A data element, composed of data items including train interval time, geographic location element identifier code, timestamp of the spatiotemporal data collection event time, latitude and longitude of the collector's geographic location at the time of the spatiotemporal data collection event, collector identifier, and time interval of the train's head and tail arriving at the same geographic location sequentially, is used to describe the attributes of the event of a train arriving at the geographic location of the data source. A data element, composed of data items including train interval time, geographic location element identifier code, timestamp of the spatiotemporal data collection event time, latitude and longitude of the collector's geographic location at the time of the spatiotemporal data collection event, collector identifier, time interval of the train's head and tail arriving at the same geographic location sequentially, and the train interval time of tracking the nearest train ahead on the same track, is used to describe the attributes of the event of a train arriving at the geographic location of the data source.

[0009] On the other hand, the present invention provides a locomotive signaling system, comprising: a signal, used to collect spatiotemporal data from a ground-based data source and simultaneously act as a sensor node and management object of a wireless sensor network, cooperating with networked signalers within the network coverage area to monitor the signal and cooperating to generate locomotive signals; and a data source, used to monitor events of trains arriving at the geographical location of the data source and to generate spatiotemporal data and output the spatiotemporal data to the train's signal.

[0010] Preferably, the spatiotemporal data includes a train tracking interval time data item, which describes the time interval between the time when the train at the signal location tracks the nearest train ahead of it to the geographical location of the data source and the time when the train at the signal location arrives at that geographical location.

[0011] Preferably, the cooperative monitoring employs one of the following data mining methods: the speed of the nearest train ahead on the same track in the train tracking operation at its current geographical location; the train integrity in the train tracking operation at its current geographical location ...

[0012] Preferably, the locomotive signal generated by the cooperative generation of the signal includes data correction of the train timetable for train control where the signal is located using a particle swarm optimization algorithm.

[0013] Preferably, the spatiotemporal data uses one of the following data elements: a data element for tracking train interval time, used to describe the attributes of the event of a train arriving at the geographical location of the data source; a data element composed of data items for tracking train interval time and geographical location element identification code, used to describe the attributes of the event of a train arriving at the geographical location of the data source; a data element composed of data items for tracking train interval time and the time interval between the arrival of the front and rear of the train at the same geographical location, used to describe the attributes of the event of a train arriving at the geographical location of the data source; a data element composed of data items for tracking train interval time, geographical location element identification code, and the time interval between the arrival of the front and rear of the train at the same geographical location, used to describe the attributes of the event of a train arriving at the geographical location of the data source; a data element composed of data items for tracking train interval time, geographical location element identification code, the time interval between the arrival of the front and rear of the train at the same geographical location, and the tracking train interval time of the train running on the same track and the nearest train ahead, used to describe the attributes of the event of a train arriving at the geographical location of the data source.

[0014] In another aspect, the present invention also provides a database, including: a geographic location element identification code data item, used to describe the attributes of the geographic location of the train operating line; and a length data item, used to describe the attributes of the train operating lines between the geographic locations of the train operating lines.

[0015] In another aspect, the present invention also provides a train timetable, including: a tracking train interval time data item, used to specify the tracking train interval time required for train operation.

[0016] Preferably, the train timetable uses one of the following data elements: a single tracking train interval time data item, used to describe the attribute of the train arriving at the geographical location of the data source; a data element composed of tracking train interval time and geographical location element identifier data items, used to specify the tracking train interval time and geographical location element identifier attribute that the train needs to achieve; a data element composed of tracking train interval time and the time interval between the train's head and tail arriving at the same geographical location event, used to specify the tracking train interval time and the time interval between the train's head and tail arriving at the same geographical location event, used to specify the tracking train interval time, geographical location element identifier, ...

[0017] The beneficial effects of this invention are as follows: This invention is a fully functional and independently operating locomotive signaling system that is compatible with existing train operation control systems. Using this invention, trains can reliably obtain the position and speed information of all trains in their vicinity, and achieve autonomous control and train operation safety through the coordinated actions of a distributed autonomous signaling group. This facilitates the implementation of automated train reception and dispatch and automated train operation, improves the throughput capacity of passenger stations on passenger dedicated lines, and helps mitigate the spread of the impact of emergencies and the loss of network capacity. Attached Figure Description

[0018] Figure 1 This is an application scenario diagram of the preferred embodiment 1 of the present invention;

[0019] Figure 2 This is a system structure diagram of a preferred embodiment 1 of the present invention;

[0020] Figure 3 This is a flowchart of a preferred embodiment of the present invention;

[0021] Figure 4 This is a timing diagram of a preferred embodiment 1 of the present invention;

[0022] Figure 5 This is a data structure diagram of the data source in a preferred embodiment 1 of the present invention;

[0023] Figure 6 This is a network database data structure diagram of a preferred embodiment of the present invention;

[0024] Figure 7 This is a data structure diagram of the train operation line database in a preferred embodiment of the present invention;

[0025] Figure 8 This is a preferred embodiment of the train timetable data structure diagram of the present invention;

[0026] Figure 9 This is a time interval diagram of a preferred embodiment 1 of the present invention;

[0027] Figure 10 This is a spatial data mining diagram of a preferred embodiment of the present invention;

[0028] Figure 11 This is a wireless sensor network coverage diagram of a preferred embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the preferred embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] like Figure 1 The diagram shown illustrates an application scenario of a preferred embodiment of the present invention. 101 represents the train running line, 102 is the data source, 103 is the signal, 104 is the train where signal 103 is located, 105 is a networked signal communicating with signal 103 via a wireless sensor network, 106 is the train where networked signal 105 is located and / or the train 104 where signal 103 is located tracking the nearest train ahead on the same track, 107 is a networked signal communicating with signal 103 via a wireless sensor network, 108 is the train where networked signal 107 is located, 109 is the radio signal used for network communication via the wireless sensor network, 110 is the radio signal used by the signal to collect spatiotemporal data from a ground-based data source, and 111 represents the direction of travel; trains 104, 106, and 107 are also shown. Train 108 is traveling on train track 101; train 106 is located at the closest geographical location ahead of train 104 on the same track, and train 108 is located at the spatially adjacent geographical location of train 104; signals 103, 105, and 107 serve as sensor nodes in a wireless sensor network, and a mobile ad hoc network is established between the nodes; signal 105 of train 106 arrives at the geographical location of the data source as train 106 travels, and signal 105 collects spatiotemporal data from the ground data source via radio signal 110; signal 103 of train 104 and signal 107 of train 108 have not arrived at the geographical location of any data source, and neither signal 103 nor signal 107 has collected spatiotemporal data from any ground data source.

[0031] like Figure 2 The diagram shown is a system structure diagram of a preferred embodiment 1 of the present invention. It includes a train-mounted signal 201 and a ground-based data source 202. The signal 201 is used to collect spatiotemporal data from the ground-based data source and simultaneously acts as a sensor node and management object in a wireless sensor network, cooperating with networked signal sources within the network coverage area to monitor the signal sources and cooperating in generating locomotive signals. The data source 202 is used to monitor the time of the train's arrival at the geographical location of the data source, generate spatiotemporal data, and output spatiotemporal data to the train-mounted signal.

[0032] The signal 201 includes an information acquisition module, an information processing module, a wireless communication module, and an energy supply module. The information acquisition module is used to acquire spatiotemporal data from ground data sources and generate the latitude and longitude of the signal's geographical location at the time of the spatiotemporal data acquisition event, the time stamp of the spatiotemporal data acquisition event, and the signal's identifier information. The information processing module is used for data mining and locomotive signal generation. The wireless communication module is used to implement mobile ad hoc network communication with networked signals within the network coverage area. The energy supply module is used to provide the energy required for the information acquisition module, information processing module, and wireless communication module to operate.

[0033] The data source 202 includes a train monitoring module, an information processing module, a wireless communication module, and an energy supply module. The train monitoring module is used to monitor the time of the train's arrival at the geographical location of the data source and to control the cumulative time interval of the information processing module. The information processing module is used, under the control of the monitoring module, to generate spatiotemporal data including the train tracking interval time and / or the generation of geographical feature identifiers and / or the generation of the time interval between the arrival of the front and rear of the train at the same geographical location and / or the generation of the train tracking interval time of the nearest train on the same track. The wireless communication module is used to transmit the spatiotemporal data information. The energy supply module is used to provide the energy required for the operation of the train monitoring module, the information processing module, and the wireless communication module.

[0034] The signal 201 serves as a node in the wireless sensor network, communicating with all other signal assemblies within the network's coverage area via a mobile ad hoc network to form the wireless sensor network. Simultaneously, it acts as a management object, coordinating the actions of a distributed autonomous signal assemblies and their communication through parallel processing of multiple signal assemblies to improve work efficiency and reduce the impact of local faults on the overall system. Together with the signal assemblies, all other signal assemblies within the wireless sensor network's coverage area, and data sources on the train's operating line, the signal 201 constitutes the locomotive signaling system based on the wireless sensor network and data sources.

[0035] like Figure 3The diagram shown is a flowchart of a preferred embodiment 1 of the present invention.The change of geographical location of the signal as the train moves includes changes in the geographical relationship between the geographical location of any signal and the geographical locations of other signals, and changes in the geographical relationship between the geographical location of any signal and the geographical location of any data source. The arrival of the interrogation signal at the geographical location of the data source as the train moves includes a determination by any data source using a comparison between the measured amplitude and / or phase of the interrogation signal and its internal amplitude and / or phase threshold. When any data source measures that the amplitude and / or phase of the interrogation signal reaches its internal amplitude and / or phase threshold, it determines that the interrogation signal has arrived at its geographical location. Upon detecting the arrival of the interrogation signal, the data source generates a radio communication signal containing spatiotemporal data and sends it to the air. The process includes: when any data source determines that an inquiry signal has arrived, the data source generates a radio communication signal including spatiotemporal data information and sends it to the air space; the signal collects spatiotemporal data from the radio communication signal, including any signal receiving the radio communication signal of spatiotemporal data output from the data source to the air space, and obtaining the spatiotemporal data after demodulation; the data source waits for the arrival of the inquiry signal output by the next signal, including the data source identifying the characteristics of the inquiry signal and waiting for the arrival of inquiry signals with different characteristics; the signal moves with the train to reach the coverage area of ​​the wireless sensor network, including any signal changing its geographical location to reach a geographical location where wireless sensor network communication can be implemented; the connection with the wireless sensor network... A large number of signal controllers within the network coverage area form a computer cluster using mobile ad hoc network communication. This includes any signal controller forming a computer cluster with signal controllers of trains ahead on the same track within a certain distance, signal controllers of trains behind on the same track within a certain distance, and signal controllers of other trains on the same track within a certain distance. Updating the network database involves signal controllers replacing corresponding old data in the network database with newly collected spatiotemporal data. Data mining of the network database using the coordinated actions of the distributed autonomous signal controller group includes spatial data mining of prior train speeds, spatial data mining of prior train integrity, spatial data mining of prior second train speeds, spatial data mining of any train speed within the network, and spatial data mining of other train speeds within the network. Spatial data mining of the integrity of any prior train, spatial data mining of the direction of travel of any train in the network, and spatial data mining of the time interval between the expected arrival of any train in the network at any preset geographical location; the data correction of the train operation diagram for train operation control by the signal using particle swarm optimization algorithm and the coordinated action of a distributed autonomous signal group includes: the signal group implementing the network in the wireless sensor network uses a random search algorithm based on group cooperation to simulate train movement as particles, so that the train continuously moves towards a better region in the solution space according to the change of its adaptability to the environment, thereby achieving the purpose of group optimization; the train is controlled by the train operation diagram, and the signal generates the locomotive signal by correcting the train operation diagram.

[0036] like Figure 4 The diagram shown is a timing diagram of a preferred embodiment 1 of the present invention. During train operation, the signal and the networked signal are connected via a wireless sensor network, which is a peer-to-peer network. The signal transmits a signal inquiry signal into the air. When the signal moves with the train to the location of the data source on the ground, the data source on the ground detects the time of the train's arrival at the data source's location. The data source generates spatiotemporal data of the signal and sends it to the signal via radio communication. The signal collects the spatiotemporal data and updates the signal network database with the collected spatiotemporal data. The network database is a peer-to-peer computing database. The networked signal transmits a networked signal inquiry signal into the air. When the networked signal moves with the train to the location of the data source on the ground, the data source on the ground detects the time of the train's arrival at the data source's location. The data source generates spatiotemporal data of the networked signal and sends it to the networked signal via radio communication. The networked signal collects the spatiotemporal data of the networked signal and updates the networked signal network database with the collected spatiotemporal data. The system employs a peer-to-peer computing database. Signal machines perform data mining on their network database, and networked signal machines perform data mining on their database. Signal machines and networked signal machines communicate via wireless sensor networks to share resources between their respective network databases. Signal machines also perform data mining on their network databases, and networked signal machines perform data mining on their databases. Furthermore, signal machines and networked signal machines communicate via wireless sensor networks to share knowledge about their respective trains. They also share resources for correcting train timetables using particle swarm optimization algorithms. Both signal machines and networked signal machines jointly correct the train timetables of the trains where the signal machine is located and the train timetables of the trains where the networked signal machine is located. Trains where the signal machine is located are controlled by the corrected train timetable, and trains where the networked signal machine is located are controlled by the corrected train timetable.

[0037] The data mining of the network database includes the speed of the preceding train, the integrity of the preceding train, the speed of the second preceding train, the speed of any train in all network signals, the integrity of any preceding train in all network signals, the direction of travel of any train in all network signals, and the time interval for any train in all network signals to reach any preset geographical location. The preset geographical location is a geographical location that needs to be calculated, described by a geographical location element identifier code.

[0038] like Figure 5 The diagram shown is a data source data structure diagram of a preferred embodiment 1 of the present invention. Figure 5 A total of five independently implementable data source structure schemes are presented. Figure 5 (a) is the single tracking train interval T used for the data source. T The first type of data element structure for data items, Figure 5 (b) is the train interval T used as the data source. T Geographic location element identification code P S The second type of data element structure in the combination, Figure 5 (c) is the train interval T used as the data source. T The time interval T between the arrival of the front and rear of the train at the same geographical location. C The third type of data element structure in the combination. Figure 5 (d) represents the train interval T used as the data source. T Geographical location element identification code P S The time interval T between the arrival of the front and rear of a train at the same geographical location. C The fourth type of data element structure in the combination Figure 5 (e) represents the train interval T used as the data source. T Geographic location element identification code P S And the time interval T between the train's front and rear locomotives arriving at the same geographical location one after the other. C The fifth data element structure is a combination of the tracking train interval time of the nearest train ahead in the train tracking operation. The above data structure is one of the five data element structures used in the locomotive signaling system based on wireless sensor networks and data sources of the present invention, and the preferred embodiment 1 adopts one of them.

[0039] like Figure 6 The diagram shown is a network database element structure diagram of a preferred embodiment of the present invention. Figure 6 A total of 10 independently implementable network database data structure schemes are presented. Figure 6 (a) Element 1 data item is the first type of data item structure used by the network database, including the tracking train interval time T. T Geographic location element identification code P S The time stamp T of the spatiotemporal data collection event time S ; Figure 6 (b) Element 2 data item is the second type of data item structure used by the network database, including the tracking train interval time T. T The latitude and longitude (N / E) of the data acquisition device at the time of the spatiotemporal data acquisition event and the time stamp (T) of the spatiotemporal data acquisition event.S ; Figure 6 (c) Element 3 is the third type of data item structure used by the network database, including the tracking train interval time T. T Geographic location element identification code P S The time interval T between the arrival of the front and rear of a train at the same geographical location. C The time stamp T of the spatiotemporal data collection event time S ; Figure 6 (d) Element 4 is the fourth type of data item structure used by the network database, including the tracking train interval time T. T Geographic location element identification code P S The time interval T between the arrival of the front and rear of a train at the same geographical location. C The latitude and longitude (N / E) of the data acquisition device at the time of the spatiotemporal data acquisition event and the time stamp (T) of the spatiotemporal data acquisition event. S ; Figure 6 (e) Element 5 is the fifth type of data item structure used by the network database, including the tracking train interval time T. T Geographic location element identification code P S and the identifier N of the collector S ; Figure 6 (f) Element 6 is the sixth type of data item structure used by the network database, including the tracking train interval time T. T The latitude and longitude (N / E) of the data acquisition device at the time of the spatiotemporal data acquisition event, and the data acquisition device identifier (N). S ; Figure 6 (g) Element 7, the seventh data item, is the structure of the seventh type of data item used in the network database, including the tracking train interval time T. T Geographic location element identification code P S The time interval T between the arrival of the front and rear of a train at the same geographical location. C and the identifier N of the collector S ; Figure 6 (h) Element 8 is the eighth data item structure used by the network database, including the tracking train interval T. T The time interval T between the arrival of the front and rear of a train at the same geographical location. C The latitude and longitude (N / E) of the data acquisition device at the time of the spatiotemporal data acquisition event, and the data acquisition device identifier (N). S ; Figure 6(i) Element 9 is the ninth type of data item structure used by the network database, including the tracking train interval time T. T Geographic location element identification code P S Time stamp T for spatiotemporal data collection events S The data acquisition event includes the latitude and longitude (N / E) of the data acquisition device's geographical location, and the time interval (T) between the arrival of the train's head and tail at the same geographical location. C and the identifier N of the collector S ; Figure 6 (j) Element 10 is the 10th data item structure of the data elements used in the network database, including the tracking train interval time T. T Geographic location element identification code P S Time stamp T for spatiotemporal data collection events S The data acquisition event includes the latitude and longitude (N / E) of the data acquisition device's geographical location, and the time interval (T) between the arrival of the train's head and tail at the same geographical location. C Train tracking operation: The interval T between the nearest train ahead on the same track. TP and the identifier N of the collector S The above data structure comprises 10 data element structures for the network database of the locomotive signaling system based on wireless sensor networks and data sources of the present invention, and one of them is adopted in this preferred embodiment 1.

[0040] like Figure 7 The diagram shown is a data structure diagram of the train operation line database in a preferred embodiment of the present invention. Figure 7 (a) is the geographic location element identifier data item P S701 represents the train operating line, 702 represents the data source, and 703 represents the geographic location element identification code. As shown in the diagram, geographic location element identification codes are assigned to various locations along the train operating line, namely 334455667712, 334455667713, 334455667714, 334455667715, 334455667716, 334455667717, 334455667718, 334455667719, 334455667720, 334455667721, and 334455667721. 55667722, 334455667723, 334455667724, 334455667725, 334455667726, 334455667727, and 334455667728 each have a data source for their respective geographic location element identification code. Each data source pre-stores the geographic location element identification code of its own location. When any data source detects that a signal has moved with the train to the location of the data source, it immediately outputs its pre-stored geographic location element identification code to the train's signal via radio communication.

[0041] Figure 7 (b) is the data item L for the length of the train operating line section. S (mm), 704 is the train operating line, 705 is the data source, and 706 is the train operating line section length data item L. S(mm), as shown in the figure, the length of the train line section from the location of the data source with pre-stored geographic location element identifier code 334455667712 to the location of the data source with pre-stored geographic location element identifier code 334455667713 is 20232, the length of the train line section from the location of the data source with pre-stored geographic location element identifier code 334455667713 to the location of the data source with pre-stored geographic location element identifier code 334455667714 is 20290, and the length of the train line section from the location of the data source with pre-stored geographic location element identifier code 334455667714 to the location of the data source with pre-stored geographic location element identifier code 334455667 is 20290. The train line section length from the location of data source 715 is 20433. The train line section length from the location of data source 334455667715 to the location of data source 334455667716 is 19683. The train line section length from the location of data source 334455667716 to the location of data source 334455667717 is 20653. The train line section length from the location of data source with pre-stored geographical location element identifier code 334455667718 to the location of data source with pre-stored geographical location element identifier code 334455667719 is 19883. The train line section length from the location of data source with pre-stored geographical location element identifier code 334455667719 to the location of data source with pre-stored geographical location element identifier code 334455667720 is 19245. The train line length between the geographical location of data source 20 and the geographical location of data source 334455667721 is 20135. The train line length between the geographical location of data source 334455667721 and the geographical location of data source 334455667722 is 20002. The train line length between the geographical location of data source 334455667722 and the geographical location of data source 334455667723 is an infinity where the line is not connected.The length of the train line between the geographical locations of data sources with pre-stored geographical location element identifiers 334455667723 and 334455667724 is 20285. The length of the train line between these locations is 20275. The length of the train line between these locations is 20285. The train line length from the location of data source 334455667726 to the location of data source 334455667727 is 20295; the train line length from the location of data source 334455667727 to the location of data source 334455667728 is 18285; and the train line length from the location of data source 334455667728 to the location of data source 334455667717 is 23290.

[0042] Figure 7 (c) is a diagram of the database element structure, including the geographic location element identifier P. S and the length L of the train running line section S Data item.

[0043] Figure 7 (d) is a database table, including geolocation feature identifiers P that describe the attributes of the two endpoints. S The data item and one train running line section length L describing the attributes between endpoints. S Data items are the rows of a table, which are data elements. The columns of a table are a database composed of sets of data elements.

[0044] like Figure 8 The diagram shown is a data structure diagram of the train operation diagram of a preferred embodiment of the present invention. Figure 8 (a) Train timetable element 1 is the first type of data element used in the train timetable, including the tracking train interval time T. T Data item; Figure 8 (b) Train timetable element 2 is the second type of data element used in the train timetable, including the tracking train interval time T. T And geographic feature identifier code Ps data item; Figure 8(c) Train timetable element 3 is the third type of data element used in the train timetable, including the tracking train interval time T. T The time interval T between the train's front and rear locomotives arriving at the same geographical location sequentially. C Data item; Figure 8 (d) Train timetable element 4 is the fourth type of data element used in the train timetable, including the tracking train interval time T. T Geographic element identification code P S The time interval T between the train's front and rear locomotives arriving at the same geographical location sequentially. C Data item; Figure 8 (e) is a tabular representation of the train timetable, corresponding to Figure 8 (d) shows the case using the fourth type of data element. The above data structures are four data element structures used in the train timetable of the locomotive signaling system based on wireless sensor networks and data sources of the present invention, and one of them is used in the preferred embodiment 1 of the present invention.

[0045] like Figure 9 The diagram shown is a time interval diagram of the present invention. Figure 9 (a) is the tracking train interval T TP and T T The diagram shows, on the timeline, the arrival times of the nearest train ahead on the same track as the signal, the arrival times of the nearest train ahead on the same track as the signal, and the arrival times of the trains at the same geographical locations. These geographical locations are the same. The interval T between the arrival times of the nearest train ahead on the same track as the signal and the arrival times of the trains at the same geographical locations are considered the following train intervals. TP The train-tracking interval T is the time interval between the arrival time of the nearest train ahead on the same track as the train located at the signal and the arrival time of the train located at the signal. T Data source monitoring Figure 9 (a) The event time is used to control an internal timer to generate the train interval T between the trains following the nearest train on the same track where the signal is located, in a cumulative time interval manner. TP The train interval T between the train and the signal. T .

[0046] Figure 9 (b) is the interval T between the first and last trains. CThe figure shows the arrival times of the train's head and tail at the same geographical location on the timeline. The time interval T between the arrival times of the train's head and tail at the same geographical location is defined as the time difference between their arrival times. C The time interval T between the first and last trains is... C Data source monitoring Figure 9 (b) The event time is controlled by an internal timer that generates the train head-to-tail interval time T by accumulating time intervals. C .

[0047] Figure 9 (c) Tracking train interval time T T The graph shows that the data source generates the train interval T using a time interval combination method. T On the timeline, there are the arrival times of the nearest train head and tail on the same track, and the arrival times of the train head on the same track. The geographical locations are the same. The time interval T between the arrival times of the nearest train head and tail on the same track is the time interval between their arrival times. C The time interval T is the time between the arrival of the tail of the nearest train ahead on the same track and the arrival of the head of the train at the same geographical location. D The train tracking interval T is the time between the arrival time of the nearest train ahead on the same track and the arrival time of the train ahead at that location. T , among which, T T =T C +T D The train's signal is determined by the data source at the time the train's locomotive arrives at its geographical location. C and T D The train's signal recorded T C and T D And calculate T T =T C +T D Data source monitoring Figure 9(c) The event time is generated by an internal timer controlled by the event time, which accumulates the time interval T between the train tracking operation and the nearest train head and tail arriving at the same geographical location on the same track. C The time interval T between the tail and locomotive of the nearest train traveling on the same track and arriving at the same geographical location. D .

[0048] Correspondingly, the tracking train interval time data item in the network database and / or the train timetable is in T format. C and T D To express.

[0049] like Figure 10 The diagram shown is a data mining diagram of a preferred embodiment 1 of the present invention. Figure 10 (a) is a layout diagram of the data source and the length of the train running line section. 1001 is the train running line, 1002 is the train, 1003 is the signal for train 1002, 1004 is the direction of travel, 1005 is the data source, 1006 is the data source, and 1007 is the data source. The length of the train running line section between data source 1005 and data source 1006 is L. S1 The length of the train running line section between data source 1005 and data source 1006 is L. S2 All passing trains collect spatiotemporal data generated by data sources 1005, 1006, and 1007 in chronological order.

[0050] Figure 10 (b) is a spatial data mining plot of the prior train speed data. On the time axis, there are the spatiotemporal data event times of the signal collecting data from the nearest train ahead on the same track as the train being tracked, data collecting data from data source 1005, data collecting data from data source 1006, data collecting data from data source 1007, and data collecting data from data source 1005. S1 The signal machine collects spatiotemporal data event time T from data source 1006. S2 The signal machine collects 1007 spatiotemporal data events T from the data source. S3 .

[0051] The time interval T between the time of the event from the time the train at the signal location is at the nearest train ahead on the same track to the time of the event from the time the train at the signal location is at ... signal location is at the time of the event from the time the train at the signal location is at the time of the event from the time the signal location is at the time of the event from the time the signal location is at the time of the event from the time the signal location is at the time of the event from the time the signal location is at the time of the event from the time the signal location is at the time of T1The train tracking interval T is the time interval between the time of the event from the time the train at the signal location collects the spatiotemporal data from data source 1006 to the time of the event from the time the train at data source 1006 is collected by the signal. T2 The train tracking interval T is calculated from the time of the event of the train tracking the nearest train on the same track, collected from data source 1007 by the signal where the train is located. T3 The data collection interval T is defined as the time between the time of the event collected from data source 1005 by the nearest train ahead on the same track as the train being tracked, and the time of the event collected from data source 1006 by the nearest train ahead on the same track as the train being tracked. SI1 The data collection interval T is defined as the time between the time of the spatiotemporal data collected from data source 1006 by the nearest train ahead on the same track where the train is located and the time of the time of the spatiotemporal data collected from data source 1007 by the nearest train ahead on the same track where the train is located. SI2 The time interval T is the time between the acquisition of spatiotemporal data events from data source 1005 by the signal and the acquisition of spatiotemporal data events from data source 1006 by the signal. SI3 The time interval T is the time between the acquisition of spatiotemporal data events from data source 1006 by the signal and the acquisition of spatiotemporal data events from data source 1007 by the signal. SI4 .

[0052] The signal is located on train T. S1 Time collected T T1 And record T s1 In T S2 Time collected T T2 And record T S2 In T S3 Time collected T T3 And record T S3 Calculate T S13 and T sI4 , among which, T SI3 =T S2 -T S1 T SI4 =T S3 -T S2 ; Calculate T SI1 and T SI2 , among which, T SI1 =T SI3 +T T1 -T T2 T SI2 =T SI4 +TT2 -T T3 ; Calculate the speed V of the nearest train ahead on the same track where the signal is located, which the train is tracking. P V P =L S2 / T SI2 , and / or V P =L S1 / T SI1 This refers to the prior spatial data mining of train speeds.

[0053] Figure 10 (c) This is a spatial data mining diagram of the prior train integrity data. On the time axis, there are the following events: the time when the train head of the nearest train ahead of the signal on the same track reaches the geographical location of data source 1006; the time when the train head of the nearest train ahead of the signal on the same track reaches the geographical location of data source 1007; the time when the tail of the nearest train ahead of the signal on the same track reaches the geographical location of data source 1006; the time when the tail of the nearest train ahead of the signal on the same track reaches the geographical location of data source 1007; and the time T when the train head of the signal reaches the geographical location of data source 1006. S4 The train's locomotive, located at the signal point, travels to the location of data source 1007, at event time T. S5 .

[0054] The time interval T is from the time the train at the signal location reaches the nearest train ahead on the same track, from the time the nearest train ahead on the same track reaches the time the nearest train ahead on the same track, to the time the nearest train ahead on the same track, reaches the time the nearest train ahead on the same track, is. SI5 The time interval T is the time interval between the arrival of the head of the nearest train ahead of the train on the same track as the signal at the geographical location of data source 1006 and the arrival of the tail of the nearest train ahead of the train on the same track as the signal at the geographical location of data source 1006. C1 The time interval T is the time interval between the arrival of the head of the nearest train ahead of the signal on the same track at the location of data source 1007 and the arrival of the tail of the nearest train ahead of the signal on the same track at the location of data source 1007. C2The data collection interval T is defined as the time from the time the train at the signal location reaches the location of the nearest train ahead on the same track (data source 1006) to the time the train at the signal location reaches the location of the nearest train ahead on the same track (data source 1007). SI6 The time interval T is the time between the arrival of the tail of the nearest train ahead on the same track as the train at the signal location at the geographical location of data source 1006 and the arrival of the head of the train at the signal location at the geographical location of data source 1006. D1 The time interval T is the time interval between the arrival of the tail of the nearest train ahead on the same track as the train at the signal location at the geographical location of data source 1007 and the arrival of the head of the train at the signal location at the geographical location of data source 1007. D2 The data collection interval T is defined as the time from when the train head at the signal point reaches the geographical location of data source 1006 to when the train head at the signal point reaches the geographical location of data source 1007. SI7 .

[0055] The signal is located on train T. S4 Time was collected from data source 1006 to T C1 and T D1 And record T S4 In T S5 Time was collected from data source 1007 to T C2 and T D2 And record T S5 Calculate T SI7 , among which, T SI7 =T S5 -T S4 ; Calculate T SI6 and T SI5 , among which, T SI6 =T SI7 +T D1 -T D2 T SIS =T SI7 +T D1 +T c1 -T D2 -T C2 Calculate the head-to-tail ratio R of the preceding train: R = T SI5 / T SI6The integrity of the preceding train is determined by the ratio R: it is determined whether 0.5 < R < 2.0 is true. If it is true, the integrity of the preceding train is determined to be good. If it is not true, the integrity of the preceding train is determined to be lost. This is the spatial data mining of the preceding train integrity. The thresholds 0.5 and 2.0 are constants preset based on the performance of the train model and the train line conditions. The values ​​can be taken according to the specific performance of the train model and the train line conditions.

[0056] Figure 10 (d) is the spatial data mining diagram of the second train's integrity. On the timeline, it sequentially shows the spatiotemporal data event times for the train following the nearest train on the same track as the signal, including data collected from data source 1005, data source 1006, data source 1007, data source 1005, data source 1006, data source 1007, data source 1007, data source 1006, data source 1007, and data source 1005. S6 The signal machine collects spatiotemporal data event time T from data source 1006. S7 The signal machine collects spatiotemporal data event time T from data source 1007. S8 .

[0057] The time interval T for tracking the nearest train ahead on the same track as the train at the signal location is calculated from the time of the event collected by the signal from data source 1005. TP1 The time from the time the train at the signal's location is tracking the nearest train on the same track ahead, to the time the train at the signal's location is tracked by the T-data event of the train at the signal's location, which is based on the time of the event collected from the 1005 data source. T4The time interval T of the train tracking the nearest train ahead on the same track, collected from data source 1006 by the signal, is calculated from the time of the event to the time of the train interval T. TP2 The time from the time of the event collected by the signal to the time of the nearest train ahead on the same track is the T-junction of the train located at the signal data source 1006. T5 The time interval T of the train tracking the nearest train ahead on the same track, collected from data source 1007 by the signal, is calculated from the time of the event to the time of the train interval T. TP3 The time from the time of the event collected by the signal to the time of the nearest train ahead on the same track is the T-junction of the train located at the signal data source 1007. T6 The time interval T is defined as the time between the passing of the nearest train on the same track as the train tracked by the signal (data source 1005) and the passing of the nearest train ahead. S8 The time interval T is defined as the time between the passing of the nearest train on the same track as the train being tracked by the signal and ... signal and the time between the passing of the nearest train on the same track as the signal and the time between the passing of the nearest train on the same track as the signal. S9 The time interval T is the time between the acquisition of spatiotemporal data events from data source 1005 by the signal and the acquisition of spatiotemporal data events from data source 1006 by the signal. S10 The time interval T is the time between the acquisition of spatiotemporal data events from data source 1006 by the signal and the acquisition of spatiotemporal data events from data source 1007 by the signal. S11 .

[0058] The signal is located on train T. S6 Time was collected from data source 1005 to T. TP1 and T T4 And record TS6 In T S7 Time was collected from 1006 data sources up to T TP2 and T T5 And record T S7 In T S8 Time was collected from data source 1007 to T TP3 and T T6 And record T S8 ; Calculate T SI10 and T SI11 , among which, T SI10 =T S7 -T S6 T SI11 =T S8 -T S7 ; Calculate T SI8 and T SI9 , among which, T SI8 =T SI10 +T T4 +T TPI -T T5 -T TP2 T SI9 =T SI11 +T T5 +T TP2 -T T 6-T TP3 Calculate the speed V of the nearest train ahead that the train at the signal position is following. PP V PP =L S2 / T SI9 , and / or V PP =L S1 / T S8 This refers to the prior spatial data mining of the second train speed.

[0059] Figure 10 (e) is a data mining plot of any train speed, with the time axis showing the spatiotemporal data events T collected by any train signal from data source 1005. S9 Any train signal collects spatiotemporal data event time T from data source 1006. S10 And the time T of the spatiotemporal data collected from the data source 1007 by any train signal S11 .

[0060] Spatiotemporal data event time T collected from data source 1005 by any train signal S9 The time T of the spatiotemporal data event from the 1006 data source collected by any train signal is as follows: S10 The sampling interval T SI12The time T of the spatiotemporal data from data source 1006 is collected by any train signal. S10 The time T of the spatiotemporal data event from the data source 1007 collected by any train signal is as follows: S11 The sampling interval T SII3 .

[0061] Any signal receiver acquires T via wireless sensor network resource sharing. S9 T S10 T S11 T sI12 and T sI13 Information, any signal can calculate the speed V of the train at any given signal: V = L S2 / T SI13 , and / or V=L S1 / T SI12 This refers to the spatial data mining of any train speed.

[0062] Figure 10 (f) is a data mining graph for any train integrity. On the time axis, there are the following events: the time when the train at any signal point reaches the geographical location of the nearest train (head) on the same track as the train it is tracking; the time when the train at any signal point reaches the geographical location of the nearest train (head) on the same track as the train it is tracking; the time when the train at any signal point reaches the geographical location of the nearest train (tail) on the same track as the train it is tracking; the time when the train at any signal point reaches the geographical location of the nearest train (tail) on the same track as the train it is tracking; and the time T when the train at any signal point reaches the geographical location of the nearest train (tail) on the same track as the train it is tracking; and the time T when the train at any signal point reaches the geographical location of the nearest train (head) on the same track as the train it is tracking. S12 The time T is the time when the train head at any signal reaches the geographical location of the data source 1007. S13 .

[0063] The time interval T is from the time the train at any signal point tracks the nearest train ahead on the same track and reaches the geographical location of data source 1006 to the time the train at any signal point tracks the nearest train ahead on the same track and reaches the geographical location of data source 1007. SI14 The time interval T is the time interval between the arrival of the head of the nearest train ahead of any signal on the same track at the location of data source 1006 and the arrival of the tail of the nearest train ahead of any signal on the same track at the location of data source 1006. C3The time interval T is the time interval between the arrival of the head of the nearest train ahead of any signal on the same track at the location of data source 1007 and the arrival of the tail of the nearest train ahead of any signal on the same track at the location of data source 1007. C4 The data collection interval T is defined as the time from the time the train at any signal point reaches the location of the nearest train ahead on the same track (data source 1006) to the time the train at any signal point reaches the location of the nearest train ahead on the same track (data source 1007). SI15 The time interval T is the time between the arrival of the tail of the nearest train ahead on the same track at any signal location and the arrival of the locomotive at the same geographical location at data source 1006. D3 The time interval T is the time interval between the arrival of the tail of the nearest train ahead on the same track at any signal location and the arrival of the locomotive at any signal location at the same geographical location. D4 The data collection interval T is defined as the time from the time the train head at any signal point reaches the geographical location of data source 1006 to the time the train head at any signal point reaches the geographical location of data source 1007. S116 .

[0064] The signal of any train located at T S12 Time was collected from data source 1006 to T C3 and T D3 And record T S12 In T S13 Time was collected from data source 1007 to T C4 and T D4 And record T S13 Any signal controller acquires T data via wireless sensor network resource sharing. S12 T S13 T C3 T C4 T D3 and T D4 Information; any signal machine calculates T sI16 , among which, TsI16 =T s13 -T S12 Any signal machine calculates T SI15 and T SI14 , among which, T SI15 =T SI16 +T D3 -T D4 T SI14 =T SI16 +T D3 +T C3 -T D4 -T C4 Any signal calculates the head-to-tail ratio R of the preceding train: R = T sI14 / T SI15 Any signal uses the ratio R to determine the integrity of the preceding train: it checks whether 0.5 < R < 2.0 is true. If true, the preceding train is considered to be intact; if not true, the preceding train is considered to be missing. This is the spatial data mining of the integrity of any preceding train. The thresholds 0.5 and 2.0 are preset constants based on the performance of the train model and the conditions of the train line. The values ​​can be adjusted according to the specific conditions of the train model and the train line.

[0065] Figure 10 (g) is a spatial data mining map for any train's direction of travel. 1008 represents the train line, 1009, 1011, and 1014 represent the trains, 1010 represents train signal 1009, 1012 represents signal 1011, 1015 represents signal 1014, 1013 represents the train's direction of travel, and 1016, 1017, 1018, 1019, 1020, 1021, 1022, and 1023 are data sources. Signal 1010 receives spatiotemporal data from data sources 1016 and 1017 during operation, while signal 1012 receives spatiotemporal data from data sources 1018, 1019, 1020, and 1021 during operation. Based on the spatiotemporal data collected from the source, the spatiotemporal data of signal 1015 is collected from data sources 1022 and 1023. Each signal records the time stamp of the spatiotemporal data collection event and / or the latitude and longitude of the geographical location of the signal at the time of the spatiotemporal data collection event. Each signal obtains all the above information through wireless sensor network resource sharing. Based on the pre-stored database describing the attributes of the train running line, each signal calculates the travel direction of train 1009 where signal 1010 is located, train 1011 where signal 1012 is located, and train 1014 where signal 1015 is located. This is the spatial data mining of the travel direction of each train.

[0066] Figure 10(h) is a data mining graph representing the time interval between the expected arrival of any train at any given geographical location. 1024 represents the train's operating line, 1025 represents the train, 1026 represents the signal for train 1025, 1027 represents the train, 1028 represents the signal for train 1027, 1029 represents the train's direction of travel, 1030 represents the train, and 1031 represents the signal for train 1030. P S1 P S2 and P S3 For geographic location element identification code, L N1 Train 1025 is expected to be operated by P S1 Starting point of travel to destination point P S2 The required time interval, L N2 Train 1030 is expected to be operated by P S3 Starting point of travel to destination point P S2 The required time interval; Train 1025 is identified by the geographic location element code P while in motion. S1 The data source collects spatiotemporal data and records the time of the collection event. Any signal obtains the above information through wireless sensor network resource sharing. Based on a pre-stored database describing the attributes of the train's running line, any signal calculates the expected train 1025 located at signal 1026, which is expected to be affected by P. S1 Starting point of travel to destination point P S2 The required time interval, minus the time interval from the time of event acquisition to the time of event calculation, yields the expected arrival time of train 1025 at the preset geographical location P. S2 Time interval T N1 Train 1030 is identified by its geographic location element code P during its journey. S3 The data source collects spatiotemporal data and records the time of the collection event. Any signal obtains the above information through wireless sensor network resource sharing. Based on a pre-stored database describing the attributes of the train's running line, any signal calculates the expected train 1030 located at signal 1031, which is expected to be affected by P. S3 Starting point of travel to destination point P S2 The required time interval, minus the time interval from the time of event acquisition to the time of event calculation, yields the expected arrival time of train 1030 at the preset geographical location P. S2 Time interval T N2 The calculation process described above is essentially spatial data mining of the time interval between the expected arrival times of any train at any preset geographical location.

[0067] like Figure 11The diagram shown is a wireless sensor network coverage diagram of a preferred embodiment of the present invention. 1101 is the train running line, 1102 is the train, 1103 is the wireless sensor network coverage area of ​​the signal, and 1104 is the train line, etc. Conventional station areas are limited to a geographical range of several kilometers, while the wireless sensor network coverage area of ​​conventional signal is set within a range of several kilometers to tens of kilometers in one direction.

[0068] It should be noted that the signal system provided by the present invention can also obtain the status information of the station interlocking equipment and / or train operation scheduling information from the network communication equipment located on the station ground. The signal uses the status information of the station interlocking equipment and the train operation scheduling information to coordinate the actions of the distributed autonomous signal group and their communication with each other. By processing multiple signals in parallel, the system can improve work efficiency and reduce the impact of local failures on the whole.

[0069] This invention aims to ensure the safety and improve the safety integrity level of locomotive signals. Compared with existing locomotive and railway signals, the locomotive signaling system, network database, train operation line database, and train timetable provided by this invention not only ensure the effectiveness of controlling the causes and consequences of hazardous events, but also leverage the collective advantages of the locomotive signaling system in areas with high train density. Except for the miniaturized and distributed data source setup, all equipment and implementation steps are located on the train itself, enabling mutual backup of data sources within the geographical area. This achieves a safe locomotive signaling system with its own train signal as the primary signal, achieving both autonomous and secure control. Furthermore, this invention enhances the throughput capacity of passenger stations on dedicated passenger lines and mitigates the spread of emergencies and the loss of network capacity through visualized and automated processing of train operation information.

[0070] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the technical scope of the present invention, and the patent protection scope of the present invention should be defined by the claims.

Claims

1. A network database containing data items distributed within a spatially adjacent train interior; and The spatially adjacent trains are connected to each other through communication links.

2. The network database as described in claim 1, wherein, The data item includes time data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

3. The network database as described in claim 1, wherein, The data item contains element identification code data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

4. The network database as described in claim 1, wherein, The data item contains identifier data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

5. The network database as described in claim 1, wherein, The data item contains latitude and longitude data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

6. The network database as described in claim 2, wherein, The time data item contains a time stamp data item; and The time-stamped data item is located in the space adjacent to the train interior.

7. The network database as described in claim 2, wherein, The time data item contains event time data items; and The event time data item is located in the space adjacent to the train's interior.

8. The network database as described in claim 2, wherein, The time data item contains a time interval data item; and The time interval data item is located inside the space adjacent to the train.

9. The network database as described in claim 3, wherein, The element identifier code contains the element identifier code of the geographical location of the data source; and The element identification code data item is located in the space adjacent to the train interior.

10. The network database as described in claim 3, wherein, The element identification code contains element identification codes pre-stored by each data source on the ground; and The element identification code data item is located in the space adjacent to the train interior.

11. The network database as described in claim 3, wherein, The element identification code contains the element identification code of the geographical location of the train operating line; and The element identification code data item is located in the space adjacent to the train interior.

12. The network database as described in claim 4, wherein, The identifier contains identifiers for trains on the same line within the distance range; and The identifier data item is located inside the train on the same line within the distance range.

13. The network database as described in claim 4, wherein, The identifier contains the identifier of the train following on the same track within the distance range; and The identifier data item is located inside a train on the same track behind within the distance range.

14. The network database as described in claim 4, wherein, The identifier contains the identifier of the train ahead on the same track within the distance range; and The identifier data item is located inside the train ahead on the same track within the distance range.

15. The network database as described in claim 5, wherein, The latitude and longitude include the geographical location of the train line; and The latitude and longitude data items are located inside the train at the geographical location where the train travels to the train operating line.

16. The network database as described in claim 5, wherein, The latitude and longitude include the latitude and longitude of station areas with train lines; and The latitude and longitude data items are located inside the train when the train arrives at a station area with equal train lines.

17. The network database as described in claim 5, wherein, The latitude and longitude include the latitude and longitude within the coverage area of ​​the wireless sensor network; and The latitude and longitude data items are located inside the train when the train arrives at an area covered by a wireless sensor network.

18. The network database as described in claim 6, wherein, The time stamp includes an internal time stamp of the train that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

19. The network database as described in claim 6, wherein, The time stamp includes the internal time stamp of the train, which operates under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

20. The network database as described in claim 6, wherein, The time stamps include internal time stamps for trains that are used to correct train timetables. and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

21. The network database as described in claim 7, wherein, The event time includes the internal event time of the train that generates locomotive signals during the train operation phase; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

22. The network database as described in claim 7, wherein, The event times include the internal event times of the train, which is controlled by the train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

23. The network database as described in claim 7, wherein, The event times include internal event times of trains that are used to correct train timetable data; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

24. The network database as described in claim 8, wherein, The time interval includes the internal time interval of the train that generates locomotive signals during the train operation phase; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

25. The network database as described in claim 8, wherein, The time interval includes the internal time interval of the train, which is controlled by the train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

26. The network database as described in claim 8, wherein, The time interval includes the internal time interval of the train for data correction of the train timetable; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

27. The network database as described in claim 9, wherein, The geographical location includes the geographical location of the train that generates locomotive signals during the train operation phase; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

28. The network database as described in claim 9, wherein, The geographical location includes the geographical location of the train that operates under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

29. The network database as described in claim 9, wherein, The geographical locations include those of trains whose train timetables have been corrected. and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

30. The network database as described in claim 10, wherein, The data source includes a data source that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

31. The network database as described in claim 10, wherein, The data source includes a data source for train operation controlled by a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

32. The network database as described in claim 10, wherein, The data source includes a data source for correcting train timetables; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

33. The network database as described in claim 11, wherein, The train includes trains that generate locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

34. The network database as described in claim 11, wherein, The train includes trains that operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

35. The network database as described in claim 11, wherein, The trains include those that correct train timetable data; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

36. The network database as described in claim 12, wherein, The term "line train" includes line trains that generate locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

37. The network database as described in claim 12, wherein, The term "line train" includes line trains whose operation is controlled by a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

38. The network database as described in claim 12, wherein, The aforementioned trains include those for which train timetables have been corrected; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

39. The network database as described in claim 13, wherein, The following trains include those that generate locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

40. The network database as described in claim 13, wherein, The following trains include those that operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

41. The network database as described in claim 13, wherein, The following trains include those that correct the train timetable data; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

42. The network database as described in claim 14, wherein, The preceding train includes the preceding train that generates locomotive signals during the train operation phase; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

43. The network database as described in claim 14, wherein, The preceding train includes a preceding train whose operation is controlled by a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

44. The network database as described in claim 14, wherein, The preceding trains include those for which data corrections have been made to the train timetable. and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

45. The network database as described in claim 15, wherein, The train operating line includes a train operating line that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

46. ​​The network database as described in claim 15, wherein, The train operating line includes train operating lines where trains operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

47. The network database as described in claim 15, wherein, The train operation lines include those for which train timetables are corrected; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

48. The network database as described in claim 16, wherein, The station area includes the station area where locomotive signals are generated during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

49. The network database as described in claim 16, wherein, The station area includes station areas where trains operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

50. The network database as described in claim 16, wherein, The station area includes station areas where train timetable data is corrected; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

51. The network database as described in claim 17, wherein, The wireless sensor network includes a wireless sensor network that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

52. The network database as described in claim 17, wherein, The wireless sensor network includes a wireless sensor network for controlling train movement according to a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

53. The network database as described in claim 17, wherein, The wireless sensor network includes a wireless sensor network for data correction of train timetables; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

54. The network database as described in any one of claims 6-53, wherein, The train includes trains whose speed spatial data mining calculations were performed prior to the train's speed. and Calculate T SI3 and T SI4 , among which, T SI3 =T S2 -T S1 T SI4 =T S3 -T S2 ; Calculate T SI1 and T SI2 , among which, T SI1 =T SI3 +T T1 -T T2 T SI2 =T SI4 +T T2 -T T3 ; Calculate the speed V of the nearest train ahead on the same track where the signal is located, which is tracking the train. P V P =L S2 / T SI2 , and / or V P =L S1 / T SI1 .

55. A network database as described in any one of claims 6-53, wherein, The train includes trains whose integrity spatial data mining calculations were performed on prior trains. and Calculate T SI7 , among which, T SI7 =T S5 -T S4 ; Calculate T SI6 and T SI5 , among which, T SI6 =T SI7 +T D1 -T D2 T SI5 =T SI7 +T D1 +T C1 -T D2 -T C2 ; Calculate the head-to-tail ratio R of the preceding train: R = T SI5 / T SI6 ; The integrity of the preceding train is determined by the ratio R.

56. The network database as described in any one of claims 6-53, wherein, The train includes calculations based on the speed space data mining of a prior second train; and Calculate T SI10 and T SI11 , among which, T SI10 =T S7 -T S6 T SI11 =T S8 -T S7 ; Calculate T SI8 and T SI9 , among which, T SI8 =T SI10 +T T4 +T TP1 -T T5 -T TP2 T SI9 =T SI11 +T T5 +T TP2 -T T6 -T TP3 ; Calculate the speed V of the nearest train ahead that the train at the signal position is following. PP V PP =L S2 / T SI9 , and / or V PP =L S1 / T S8 .

57. The network database as described in any one of claims 6-53, wherein, The train includes any train speed spatial data mining calculation; and Any signal receiver acquires T via wireless sensor network resource sharing. S9 T S10 T S11 T SI12 and T SI13 information; The speed V of the train at any given signal can be calculated as follows: V = L S2 / T SI13 , and / or V=L S1 / TS I12 .

58. The network database as described in any one of claims 6-53, wherein, The train includes any train whose integrity spatial data mining calculations are performed on the train; and Any signal receiver acquires T via wireless sensor network resource sharing. S12 T S13 T C3 T C4 T D3 and T D4 information; Any signal calculates T SI16 , among which, T SI16 =T S13 -T s12 ; Any signal calculates T SI15 and T SI14 , among which, T SI15 =T SI16 +T D3 -T D4 T SI14 =T SI16 +T D3 +T C3 -T D4 -T C4 ; Any signal calculates the head-to-tail ratio R of the preceding train: R = T SI14 / T SI15 ; Any signal determines the integrity of the preceding train by the ratio R.

59. A network database as described in any one of claims 6-53, wherein, The train includes calculations of spatial data mining in any train's direction of travel; and Any signal record the time stamp of the spatiotemporal data acquisition event and / or the latitude and longitude of the signal's geographical location at the time of the spatiotemporal data acquisition event; Any signal controller can acquire all of the above information through wireless sensor network resource sharing; Based on a pre-stored database describing the attributes of the train's running line, any signal can calculate the direction of travel for train 1009 (where signal 1010 is located), train 1011 (where signal 1012 is located), and train 1014 (where signal 1015 is located).

60. A network database as described in any one of claims 6-53, wherein, The train includes a train whose spatial data mining calculation is performed on the time interval between any train's expected travel to any preset geographical location. and Any signal device acquires information through wireless sensor network resource sharing; Any signal, based on a pre-stored database describing the attributes of the train's running line, calculates the expected train 1030 located at signal 1031, which is expected to be dispatched by P. S3 Starting point of travel to destination point P S2 The required time interval; Subtracting the time interval between the time of the collected event and the time of the calculated event, the expected arrival time of train 1030 at the preset geographical location P is obtained. S2 Time interval T N2 .

61. A network database system containing data items distributed spatially adjacent to the interior of a train; and The spatially adjacent trains are connected to each other through communication links.

62. The network database system as described in claim 61, wherein, The data item includes time data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

63. The network database system as described in claim 61, wherein, The data item contains element identification code data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

64. The network database system as described in claim 61, wherein, The data item contains identifier data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

65. The network database system as described in claim 61, wherein, The data item contains latitude and longitude data items, which are distributed spatially adjacent to the interior of the train; and The spatially adjacent trains are connected to each other through communication links.

66. The network database system as described in claim 62, wherein, The time data item contains a time stamp data item; and The time-stamped data item is located in the space adjacent to the train interior.

67. The network database system as described in claim 62, wherein, The time data item contains event time data items; and The event time data item is located in the space adjacent to the train's interior.

68. The network database system as described in claim 62, wherein, The time data item contains a time interval data item; and The time interval data item is located inside the space adjacent to the train.

69. The network database system as described in claim 63, wherein, The element identifier code contains the element identifier code of the geographical location of the data source; and The element identification code data item is located in the space adjacent to the train interior.

70. The network database system as described in claim 63, wherein, The element identification code contains element identification codes pre-stored by each data source on the ground; and The element identification code data item is located in the space adjacent to the train interior.

71. The network database system as described in claim 63, wherein, The element identification code contains the element identification code of the geographical location of the train operating line; and The element identification code data item is located in the space adjacent to the train interior.

72. The network database system as described in claim 64, wherein, The identifier contains identifiers for trains on the same line within the distance range; and The identifier data item is located inside the train on the same line within the distance range.

73. The network database system as described in claim 64, wherein, The identifier contains the identifier of the train following on the same track within the distance range; and The identifier data item is located inside a train on the same track behind within the distance range.

74. The network database system as described in claim 64, wherein, The identifier contains the identifier of the train ahead on the same track within the distance range; and The identifier data item is located inside the train ahead on the same track within the distance range.

75. The network database system as described in claim 65, wherein, The latitude and longitude include the geographical location of the train line; and The latitude and longitude data items are located inside the train at the geographical location where the train travels to the train operating line.

76. The network database system as described in claim 65, wherein, The latitude and longitude include the latitude and longitude of station areas with train lines; and The latitude and longitude data items are located inside the train when the train arrives at a station area with equal train lines.

77. The network database system as described in claim 65, wherein, The latitude and longitude include the latitude and longitude within the coverage area of ​​the wireless sensor network; and The latitude and longitude data items are located inside the train when the train arrives at an area covered by a wireless sensor network.

78. The network database system as described in claim 66, wherein, The time stamp includes the internal time stamp of the train that generates locomotive signals during the train operation phase: and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

79. The network database system as described in claim 66, wherein, The time stamp includes the internal time stamp of the train, which operates under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

80. The network database system as described in claim 66, wherein, The time stamps include internal time stamps for trains that are used to correct train timetables. and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

81. The network database system as described in claim 67, wherein, The event time includes the internal event time of the train that generates locomotive signals during the train operation phase; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

82. The network database system as described in claim 67, wherein, The event times include the internal event times of the train, which is controlled by the train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

83. The network database system as described in claim 67, wherein, The event times include internal event times of trains that are used to correct train timetable data; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

84. The network database system as described in claim 68, wherein, The time interval includes the time interval within the train during which locomotive signals are generated during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

85. The network database system as described in claim 68, wherein, The time interval includes the internal time interval of the train, which is controlled by the train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

86. The network database system as described in claim 68, wherein, The time interval includes the internal time interval of the train for data correction of the train timetable; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

87. The network database system as described in claim 69, wherein, The geographical location includes the geographical location where locomotive signals are generated during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

88. The network database system as described in claim 69, wherein, The geographical location includes the geographical location where the train operates under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

89. The network database system as described in claim 69, wherein, The geographical locations include those used for data correction of train timetables; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

90. The network database system as described in claim 70, wherein, The data source includes a data source that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

91. The network database system as described in claim 70, wherein, The data source includes a data source for train operation controlled by a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

92. The network database system as described in claim 70, wherein, The data source includes a data source for correcting train timetables; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

93. The network database system as described in claim 71, wherein, The train includes trains that generate locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

94. The network database system as described in claim 71, wherein, The train includes trains that operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

95. The network database system as described in claim 71, wherein, The trains include those that correct train timetable data; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

96. The network database system as described in claim 72, wherein, The term "line train" includes line trains that generate locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

97. The network database system as described in claim 72, wherein, The term "line train" includes line trains whose operation is controlled by a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

98. The network database system as described in claim 72, wherein, The aforementioned trains include those for which train timetables have been corrected; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

99. The network database system as described in claim 73, wherein, The following trains include those that generate locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

100. The network database system as described in claim 73, wherein, The following trains include those that operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

101. The network database system as described in claim 73, wherein, The following trains include those that correct the train timetable data; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

102. The network database system as described in claim 74, wherein, The preceding train includes the preceding train that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

103. The network database system as described in claim 74, wherein, The preceding train includes a preceding train whose operation is controlled by a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

104. The network database system as described in claim 74, wherein, The preceding trains include those for which data corrections have been made to the train timetable. and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

105. The network database system as described in claim 75, wherein, The train operating line includes a train operating line that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

106. The network database system as described in claim 75, wherein, The train operating line includes train operating lines where trains operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

107. The network database system as described in claim 75, wherein, The train operation lines include those for which train timetables are corrected; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

108. The network database system as described in claim 76, wherein, The station area includes the station area where locomotive signals are generated during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

109. The network database system as described in claim 76, wherein, The station area includes station areas where trains operate under train timetable control; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

110. The network database system as described in claim 76, wherein, The station area includes station areas where train timetable data is corrected; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

111. The network database system as described in claim 77, wherein, The wireless sensor network includes a wireless sensor network that generates locomotive signals during train operation; and The generated locomotive signal involves spatial data mining of the time interval between the expected arrival times of any train within the network at any preset geographical location.

112. The network database system as described in claim 77, wherein, The wireless sensor network includes a wireless sensor network for controlling train movement according to a train timetable; and The train timetable contains a tracking train interval time data item, which is used to specify the tracking train interval time that a train needs to reach.

113. The network database system as described in claim 77, wherein, The wireless sensor network includes a wireless sensor network for data correction of train timetables; and The train timetable for train operation control is corrected by using particle swarm optimization algorithm and the coordinated action of distributed autonomous signal groups.

114. The network database system as described in any one of claims 66-113, wherein, The train includes trains whose speed spatial data mining calculations were performed prior to the train's speed. and Calculate T SI3 and T SI4 , among which, T SI3 =T S2 -T S1 T SI4 =T S3 -T S2 ; Calculate T SI1 and T SI2 , among which, T SI1 =T SI3 +T T1 -T T2 T SI2 =T SI4 +T T2 -T T3 ; Calculate the speed V of the nearest train ahead on the same track where the signal is located, which is tracking the train. P V P =L S2 / T SI2 , and / or V P =L S1 / T SI1 .

115. The network database system as described in any one of claims 66-113, wherein, The train includes trains whose integrity spatial data mining calculations were performed on prior trains. and Calculate T sI7 , among which, T SI7 =T s5 -T S4 Calculate T SI6 and T SI5 , among which, T SI6 =T SI7 +T D1 -T D2 T SI5 =T SI7 +T D1 +T C1 -T D2 -T C2 ; Calculate the head-to-tail ratio R of the preceding train: R = T SI5 / T SI6 ; The integrity of the preceding train is determined by the ratio R.

116. The network database system as described in any one of claims 66-113, wherein, The train includes calculations based on the speed space data mining of a prior second train; and Calculate T SI10 and T SI11 , among which, T SI10 =T S7 -T S6 T SI11 =T S8 -T S7 ; Calculate T SI8 and T SI9 , among which, T SI8 =T SI10 +T T4 +T TP1 -T T5 -T TP2 T SI9 =T SI11 +T T5 +T TP2 -T T6 -T TP3 ; Calculate the speed V of the nearest train ahead that the train at the signal position is following. PP V PP =L S2 / T SI9 , and / or V PP =L S1 / T S8 .

117. The network database system as described in any one of claims 66-113, wherein, The train includes any train speed spatial data mining calculation; and Any signal receiver acquires T via wireless sensor network resource sharing. S9 T S10 T S11 T SI12 and T SI13 information; The speed V of the train at any given signal can be calculated as follows: V = L S2 / T SI13 , and / or V=L S1 / T SI12 .

118. The network database system as described in any one of claims 66-113, wherein, The train includes any train whose integrity spatial data mining calculations were performed; and Any signal receiver acquires T via wireless sensor network resource sharing. S12 T S13 T C3 T C4 T D3 and T D4 information; Any signal calculates T SI16 , among which, T SI16 =T S13 -T S12 ; Any signal calculates T SI15 and T SI14 , among which, T SI15 =T SI16 +T D3 -T D4 T SI14 =T SI16 +T D3 +T C3 -T D4 -T C4 Any signal calculates the head-to-tail ratio R of the preceding train: R = T SI14 / T SI15 ; Any signal determines the integrity of the preceding train by the ratio R.

119. The network database system as described in any one of claims 66-113, wherein, The train includes calculations of spatial data mining in any train's direction of travel; and Any signal record the time stamp of the spatiotemporal data acquisition event and / or the latitude and longitude of the signal's geographical location at the time of the spatiotemporal data acquisition event; Any signal controller can acquire all of the above information through wireless sensor network resource sharing; Based on a pre-stored database describing the attributes of the train's running line, any signal can calculate the direction of travel for train 1009 (where signal 1010 is located), train 1011 (where signal 1012 is located), and train 1014 (where signal 1015 is located).

120. The network database system as described in any one of claims 66-113, wherein, The train includes a train whose spatial data mining calculation is performed on the time interval between any train's expected travel to any preset geographical location. and Any signal device acquires information through wireless sensor network resource sharing; Any signal, based on a pre-stored database describing the attributes of the train's running line, calculates the expected train 1030 located at signal 1031, which is expected to be dispatched by P. S3 Starting point of travel to destination point P S2 The required time interval; Subtracting the time interval between the time of the collected event and the time of the calculated event, the expected arrival time of train 1030 at the preset geographical location P is obtained. S2 Time interval T N2 .

121. A method for assembling a network database, comprising: Pre-store data items inside the train; Trains move to change their geographical location, placing them in spatially adjacent locations, and trains are connected by communication with each other.

122. The method as described in claim 121, wherein the pre-storage includes: Pre-store time data items inside the train.

123. The method as described in claim 121, wherein the pre-storage includes: The element identification code data items are pre-stored inside the train.

124. The method of claim 121, wherein the pre-storage includes: Identifier data items are pre-stored inside the train.

125. The method of claim 121, wherein the pre-storage includes: Latitude and longitude data items are pre-stored inside the train.

126. The method of claim 122, wherein the pre-storage includes: Pre-store time stamp data items inside the train.

127. The method as described in claim 122, wherein the pre-storage includes: Pre-store event time data items inside the train.

128. The method of claim 122, wherein the pre-storage includes: Time interval data items are pre-stored inside the train.

129. The method as described in claim 123, wherein the pre-storage includes: The element identification code data item of the geographical location of the data source is pre-stored inside the train.

130. The method of claim 123, wherein the pre-storage includes: Inside the train, the element identification code data items of each data source on the ground are pre-stored.

131. The method as described in claim 123, wherein the pre-storage includes: The train's internal structure stores the element identification code data item representing the geographical location of the train's operating line.

132. The method as described in claim 124, wherein the pre-storage includes: Identifiers for trains on the same line within the pre-stored distance range.

133. The method as described in claim 124, wherein the pre-storage includes: The identifier of the following train on the same track within the pre-stored distance range.

134. The method as described in claim 124, wherein the pre-storage includes: The identifier of the train ahead on the same track within the pre-stored distance range.

135. The method of claim 125, wherein the pre-storage includes: The latitude and longitude of the geographical location of the train operating line are stored in advance.

136. The method of claim 125, wherein the pre-storage includes: The latitude and longitude of the station area with train lines are pre-stored.

137. The method of claim 125, wherein the pre-storage includes: The latitude and longitude coordinates of the wireless sensor network coverage area are pre-stored.

138. The method as described in any one of claims 126-137, wherein the pre-storage includes: Train movement generates pre-stored locomotive signals during the train operation phase.

139. The method as described in any one of claims 126-137, wherein the pre-storage includes: Train movement is controlled by a pre-stored train timetable.

140. The method of any one of claims 126-137, wherein the pre-storage includes: Pre-stored data for correcting train timetables as the train moves.

141. The method of claim 121, wherein the connection comprises: A connection for generating locomotive signals is generated during train operation.

142. The method of claim 121, wherein the connection comprises: The trains are connected and their movement is controlled by the train timetable.

143. The method of claim 121, wherein the connection comprises: A connection for correcting train timetable data.

144. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection of prior train speed spatial data mining; and Calculate T S13 and T S14 , among which, T SI3 =T S2 -T S1 T SI4 =T S3 -T S2 ; Calculate T SI1 and T SI2 , among which, T SI1 =T SI3 +T T1 -T T2 T SI2 =T S14 +T T2 -T T3 ; Calculate the speed V of the nearest train ahead on the same track where the signal is located, which is tracking the train. P V P =L S2 / T SI2 , and / or V P =L S1 / T SI1 .

145. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection of prior train integrity spatial data mining; and Calculate T SI7 , among which, T SI7 =T S5 -T S4 ; Calculate T SI6 and T SI5 , among which, T SI6 =T S17 +T D1 -T D2 T SI5 =T S17 +T D1 +T C1 -T D2 -T C2 ; Calculate the head-to-tail ratio R of the preceding train: R = T SI5 / T SI6 ; The integrity of the preceding train is determined by the ratio R.

146. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection of prior second train speed spatial data mining; and Calculate T SI10 and T SI11 , among which, T SI10 =T S7 -T S6 T SI11 =T S8 -T S7 ; Calculate T SI8 and T SI9 , among which, T SI8 =T SI10 +T T4 +T TP1 -T T5 -T TP2 T SI9 =T SI11 +T T5 +T TP2 -T T6 -T TP3 ; Calculate the speed V of the nearest train ahead that the train at the signal position is following. PP V PP =L S2 / T S19 , and / or V PP =L S1 / T S8 .

147. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection for spatial data mining of any train speed; and Any signal receiver acquires T via wireless sensor network resource sharing. S9 T S10 T SI1 T SI12 and T SI13 information; The speed V of the train at any given signal can be calculated as follows: V = L S2 / T SI13 , and / or V=L S1 / T SI12 .

148. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection of spatial data mining of any prior train integrity; and Any signal receiver acquires T via wireless sensor network resource sharing. S12 T S13 T C3 T C4 T D3 and T D4 information; Any signal calculates T SI16 , among which, T SI16 =T S13 -T S12 ; Any signal calculates T SI15 and T SI14 , among which, T SI15 =T SI16 +T D3 -T D4 T SI14 =T SI16 +T D3 + T C3 -T D4 -T C4 ; Any signal calculates the head-to-tail ratio R of the preceding train: R = T SI14 / T SI15 ; Any signal determines the integrity of the preceding train by the ratio R.

149. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection of spatial data mining for any train's direction of travel; and Any signal record the time stamp of the spatiotemporal data acquisition event and / or the latitude and longitude of the signal's geographical location at the time of the spatiotemporal data acquisition event; Any signal controller can acquire all of the above information through wireless sensor network resource sharing; Based on a pre-stored database describing the attributes of the train's running line, any signal can calculate the direction of travel for train 1009 (where signal 1010 is located), train 1011 (where signal 1012 is located), and train 1014 (where signal 1015 is located).

150. The method as described in claim 141, 142, or 143, wherein the connection comprises: The computational connection of spatial data mining for the time interval between the expected arrival of any train at any preset geographical location; and Any signal device acquires information through wireless sensor network resource sharing; Any signal, based on a pre-stored database describing the attributes of the train's running line, calculates the expected train 1030 located at signal 1031, which is expected to be dispatched by P. S3 Starting point of travel to destination point P S2 The required time interval; Subtracting the time interval between the time of the collected event and the time of the calculated event, the expected arrival time of train 1030 at the preset geographical location P is obtained. S2 Time interval T N2 .

151. A method for constructing a locomotive signaling system, comprising: Pre-store data items inside the train; Trains move to change their geographical location, placing them in spatially adjacent locations, and trains are connected by communication with each other.

152. The method as described in claim 151, wherein the pre-storage includes: Pre-store time data items inside the train.

153. The method as described in claim 151, wherein the pre-storage includes: The element identification code data items are pre-stored inside the train.

154. The method of claim 151, wherein the pre-storage includes: Identifier data items are pre-stored inside the train.

155. The method of claim 151, wherein the pre-storage includes: Latitude and longitude data items are pre-stored inside the train.

156. The method of claim 152, wherein the pre-storage includes: Pre-store time stamp data items inside the train.

157. The method of claim 152, wherein the pre-storage includes: Pre-store event time data items inside the train.

158. The method of claim 152, wherein the pre-storage includes: Time interval data items are pre-stored inside the train.

159. The method of claim 153, wherein the pre-storage includes: The element identification code data item of the geographical location of the data source is pre-stored inside the train.

160. The method of claim 153, wherein the pre-storage includes: Inside the train, the element identification code data items of each data source on the ground are pre-stored.

161. The method as described in claim 153, wherein the pre-storage includes: The train's internal structure stores the element identification code data item representing the geographical location of the train's operating line.

162. The method as described in claim 154, wherein the pre-storage includes: Identifiers for trains on the same line within the pre-stored distance range.

163. The method of claim 154, wherein the pre-storage includes: The identifier of the following train on the same track within the pre-stored distance range.

164. The method of claim 154, wherein the pre-storage includes: The identifier of the train ahead on the same track within the pre-stored distance range.

165. The method of claim 155, wherein the pre-storage includes: The latitude and longitude of the geographical location of the train operating line are stored in advance.

166. The method of claim 155, wherein the pre-storage includes: The latitude and longitude of the station area with train lines are pre-stored.

167. The method as described in claim 155, wherein the pre-storage includes: The latitude and longitude coordinates of the wireless sensor network coverage area are pre-stored.

168. The method as described in any one of claims 156-167, wherein the pre-storage includes: Train movement generates pre-stored locomotive signals during the train operation phase.

169. The method as described in any one of claims 156-167, wherein the pre-storage includes: Train movement is controlled by a pre-stored train timetable.

170. The method as described in any one of claims 156-167, wherein the pre-storage includes: Pre-stored data for correcting train timetables as the train moves.

171. The method of claim 151, wherein the connection comprises: A connection for generating locomotive signals is generated during train operation.

172. The method of claim 151, wherein the connection comprises: The trains are connected and their movement is controlled by the train timetable.

173. The method of claim 151, wherein the connection comprises: A connection for correcting train timetable data.

174. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection of prior train speed spatial data mining; and Calculate T SI3 and T SI4 , among which, T SI3 =T S2 -T S1 T SI4 =T S3 -T S2 Calculate T SI1 and T SI2 , among which, T SI1 =T SI3 +T T1 - T T2 T SI2 =T SI4 +T T2 -T T3 Calculate the speed V of the nearest train ahead on the same track where the signal is located, which is tracking the train. P V P =L S2 / T SI2 , and / or V P =L S1 / T SI1 .

175. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection of prior train integrity spatial data mining; and Calculate T SI7 , among which, T SI7 =T S5 -T S4 ; Calculate T SI6 and T SI5 , among which, T SI6 =T SI7 +T D1 -T D2 T SI5 =T s17 +T D1 +T C1 -T D2 -T C2 ; Calculate the head-to-tail ratio R of the preceding train: R = T SI5 / T SI6 ; The integrity of the preceding train is determined by the ratio R.

176. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection of prior second train speed spatial data mining; and Calculate T SI10 and T SI11 , among which, T SI10 =T S7 -T S6 T SI11 =T S8 -T S7 ; Calculate T SI8 and T SI9 , among which, T SI8 =T SI10 +T T4 +T TP1 -T T5 -T TP2 T ST9 =T SI11 +T T5 +T TP2 -T T6 -T TP3 ; Calculate the speed V of the nearest train ahead that the train at the signal position is following. PP V PP =L S2 / T SI9 , and / or V PP =L S1 / T S8 .

177. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection for spatial data mining of any train speed; and Any signal receiver acquires T via wireless sensor network resource sharing. S9 T S10 T S11 T SI12 and T SI13 information; The speed V of the train at any given signal can be calculated as follows: V = L S2 / T SI13 , and / or V=L S1 / T SI12 .

178. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection of spatial data mining of any prior train integrity; and Any signal receiver acquires T via wireless sensor network resource sharing. S12 T S13 T C3 T C4 T D3 and T D4 information; Any signal calculates T SI16 , among which, T SI16 =T S13 -T S12 ; Any signal calculates T SI15 and T SI14 , among which, T SI15 =T SI16 +T D3 -T D4 T SI14 =T SI16 +T D3 +T C3 -T D4 -T C4 ; Any signal calculates the head-to-tail ratio R of the preceding train: R = T SI14 / T SI15 ; Any signal determines the integrity of the preceding train by the ratio R.

179. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection of spatial data mining for any train's direction of travel; and Any signal record the time stamp of the spatiotemporal data acquisition event and / or the latitude and longitude of the signal's geographical location at the time of the spatiotemporal data acquisition event; Any signal controller can acquire all of the above information through wireless sensor network resource sharing; Based on a pre-stored database describing the attributes of the train's running line, any signal can calculate the direction of travel for train 1009 (where signal 1010 is located), train 1011 (where signal 1012 is located), and train 1014 (where signal 1015 is located).

180. The method as described in claim 171, 172, or 173, wherein the connection comprises: The computational connection of spatial data mining for the time interval between the expected arrival of any train at any preset geographical location; and Any signal device acquires information through wireless sensor network resource sharing; Any signal, based on a pre-stored database describing the attributes of the train's running line, calculates the expected train 1030 located at signal 1031, which is expected to be dispatched by P. S3 Starting point of travel to destination point P S2 The required time interval; Subtracting the time interval between the time of the collected event and the time of the calculated event, the expected arrival time of train 1030 at the preset geographical location P is obtained. S2 Time interval T N2 .

181. A method for controlling train operation according to a train timetable, comprising: Pre-store data items inside the train; Trains move to change their geographical location, placing them in spatially adjacent locations, and trains are connected by communication with each other.

182. The method as described in claim 181, wherein the pre-storage includes: Pre-store time data items inside the train.

183. The method of claim 181, wherein the pre-storage includes: The element identification code data items are pre-stored inside the train.

184. The method of claim 181, wherein the pre-storage includes: Identifier data items are pre-stored inside the train.

185. The method of claim 181, wherein the pre-storage includes: Latitude and longitude data items are pre-stored inside the train.

186. The method of claim 182, wherein the pre-storage includes: Pre-store time stamp data items inside the train.

187. The method of claim 182, wherein the pre-storage includes: Pre-store event time data items inside the train.

188. The method of claim 182, wherein the pre-storage includes: Pre-store interval data items inside the train.

189. The method of claim 183, wherein the pre-storage includes: The element identification code data item of the geographical location of the data source is pre-stored inside the train.

190. The method of claim 183, wherein the pre-storage includes: Inside the train, the element identification code data items of each data source on the ground are pre-stored.

191. The method of claim 183, wherein the pre-storage includes: The train's internal structure stores the element identification code data item representing the geographical location of the train's operating line.

192. The method as described in claim 184, wherein the pre-storage includes: Identifiers for trains on the same line within the pre-stored distance range.

193. The method of claim 184, wherein the pre-storage includes: The identifier of the following train on the same track within the pre-stored distance range.

194. The method of claim 184, wherein the pre-storage includes: The identifier of the train ahead on the same track within the pre-stored distance range.

195. The method of claim 185, wherein the pre-storage includes: The latitude and longitude of the geographical location of the train operating line are stored in advance.

196. The method of claim 185, wherein the pre-storage includes: The latitude and longitude of the station area with train lines are pre-stored.

197. The method as described in claim 185, wherein the pre-storage includes: The latitude and longitude coordinates of the wireless sensor network coverage area are pre-stored.

198. The method as described in any one of claims 186-197, wherein the pre-storage includes: Train movement generates pre-stored locomotive signals during the train operation phase.

199. The method as described in any one of claims 186-197, wherein the pre-storage includes: Train movement is controlled by a pre-stored train timetable.

200. The method as described in any one of claims 186-197, wherein the pre-storage includes: Pre-stored data for correcting train timetables as the train moves.

201. The method of claim 181, wherein the connection comprises: A connection for generating locomotive signals is generated during train operation.

202. The method of claim 181, wherein the connection comprises: The trains are connected and their movement is controlled by the train timetable.

203. The method of claim 181, wherein the connection comprises: A connection for correcting train timetable data.

204. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection of prior train speed spatial data mining; and Calculate T SI3 and T SI4 , among which, T SI3 =T S2 -T S1 T SI4 =T S3 -T S2 ; Calculate T SI1 and T SI2 , among which, T SI1 =T SI3 +T T1 -T T2 T S12 =T SI4 +T T2 -T T3 ; Calculate the speed V of the nearest train ahead on the same track where the signal is located, which is tracking the train. P V P =L S2 / T SI2 , and / or V P =L S1 / T SI1 .

205. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection of prior train integrity spatial data mining; and Calculate T SI7 , among which, T SI7 =T S5 -T S4 ; Calculate T SI6 and T SI5 , among which, T SI6 =T SI7 +T D1 -T D2 T S15 =T S17 +T D1 +T C1 -T D2 -T C2 ; Calculate the head-to-tail ratio R of the preceding train: R = T SI5 / T SI6 ; The integrity of the preceding train is determined by the ratio R.

206. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection of prior second train speed spatial data mining; and Calculate T SI10 and T SI11 , among which, T SI10 =T S7 -T S6 T SI11 =T S8 -T S7 ; Calculate T SI8 and T SI9 , among which, T SI8 =T SI10 +T T4 +T TP1 -T T5 -T TP2 T sT9 =T SI11 +T T5 +T TP2 -T T6 -T TP3 ; Calculate the speed V of the nearest train ahead that the train at the signal position is following. PP V PP =L S2 / T SI9 , and / or V PP =L S1 / T S8 .

207. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection for spatial data mining of any train speed; and Any signal receiver acquires T via wireless sensor network resource sharing. S9 T S10 T S11 T SI12 and T SI13 information; The speed V of the train at any given signal can be calculated as follows: V = L S2 / T SI13 , and / or V=L S1 / T SI12 .

208. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection of spatial data mining of any prior train integrity; and Any signal receiver acquires T via wireless sensor network resource sharing. S12 T S13 T C3 T C4 T D3 and T D4 information; Any signal calculates T SI16 , among which, T SI16 =T S13 -T S12 ; Any signal calculates T SI15 and T SI14 , among which, T SI15 =T SI16 +T D3 -T D4 T SI14 =T SI16 +T D3 +T C3 -T D4 -T C4 ; Any signal calculates the head-to-tail ratio R of the preceding train: R = T sI14 / T SI15 ; Any signal determines the integrity of the preceding train by the ratio R.

209. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection of spatial data mining for any train's direction of travel; and Any signal record the time stamp of the spatiotemporal data acquisition event and / or the latitude and longitude of the signal's geographical location at the time of the spatiotemporal data acquisition event; Any signal controller can acquire all of the above information through wireless sensor network resource sharing; Based on a pre-stored database describing the attributes of the train's running line, any signal can calculate the direction of travel for train 1009 (where signal 1010 is located), train 1011 (where signal 1012 is located), and train 1014 (where signal 1015 is located).

210. The method as described in claim 201, 202 or 203, wherein the connection comprises: The computational connection of spatial data mining for the time interval between the expected arrival of any train at any preset geographical location; and Any signal device acquires information through wireless sensor network resource sharing; Any signal, based on a pre-stored database describing the attributes of the train's running line, calculates the expected train 1030 located at signal 1031, which is expected to be dispatched by P. S3 Starting point of travel to destination point P S2 The required time interval; Subtracting the time interval between the time of the collected event and the time of the calculated event, the expected arrival time of train 1030 at the preset geographical location P is obtained. S2 Time interval T N2 .

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